However, maintaining format consistency with earlier CoNLL tasks was deemed convenient for sites that already had tools configured to deal with that format.

in order to distribute the data so that one could make the best of both worlds, we created a new file extension -.conll which logically served as another layer in addition to the .parse, .prop, .sense, .name and .coref layers which house the respective annotations.


The test data released from LDC did not contain the coreference layer.

this year unlike previous CoNLL tasks, the test data contained some truly unseen documents.


The choice was not simple, and after having consulted various researchers in the field, we came to a conclusion that each metric had its pros and cons and there is no silver bullet.

we settled on the MELA metric proposed by Denis and Baldridge (2009), which takes a weighted average of three metrics: MUC, B-CUBED, and CEAF.


We wanted to encourage researchers to run their systems on all three languages.

we decided to compute the final official score that would determine the winning submission as the average of the MELA metric across all the three languages.


To embed classic cluster metrics in this objective function is practically infeasible since most of such metrics lead to NP-hard optimization problems.

we introduce coreference trees in order to represent a cluster by a directed tree over its mentions.


Coreference trees are not given in the training data.

we assume that these structures are latent and make use of the latent structure perceptron (Fernandes and Brefeld, 2011; Yu and Joachims, 2009) to train our models.


Intuitively, the performance of coreference resolver on English should perform better than that on Chinese and Arabic since we have substantial corpus for English and coreference resolution on English is well studied compared to another two languages.

we could imagine that projecting the results from English into Chinese and Arabic should still beats the baseline system using monolingual resolution method.


In last year's evaluation task, the Standford Natural Language Processing Group ranked the first position and they also open their toolkit for research community, namely Standford CoreNLP (Lee et al., 2011) 1 , better yet, their toolkit is optimized for CoNLL task.

we could use their toolkit as our English resolver and concentrate on bettering the projection of coreferring entities.


Clearly, there is an edge with tuple (1 − p(i, j), 1) between each entity end mention indicating that each entity could map into any mention while with different probabilities.

solving this Cost-Flow problem is equal to maximizing the equation 1 with two constraints.


Including a feature representing the class assigned by the singletons classifier for each anaphor triggers a conservative learner behavior, i.e., fewer positive classes are assigned.

to account for this behavior, we ignore those labels for the anaphor and include only one feature (no.


This is due to the fact that during mention detection, we aim for high recall, and after coreference resolution, all mentions identified as singletons by the system are excluded from the answer set.

mentions that are coreferent in the key set but wrongly classified in the answer set are removed, leading to a decrease in recall.


The results for this set of experiments is based on a version of the test set that contains the gold boundaries of all mentions, including singletons.

we use these gold mention boundaries instead of the ones generated by our system.


• Mention Detection Sieve: We in this sieve first extract all the noun phrases, pronouns (the words with part-of-speech (POS) tag PN), proper nouns (the words with POS tag NR) and named entities.


Different from English POS tags, in Chinese plural nouns couldn't be distinguished from single nouns in terms of the POS.

we add two rules to judge whether a noun is plural or not.


In order to serve as a basis for further work, the components of our system were designed to work as simple as possible.

our system relies mostly on local information between pairs of mentions.


The pairs of these mentions in one document are regarded as the coreference candidates.

the high recall is a more important target than higher precision for this stage.


The evaluation on development data shows that the achieved precision of our system is better than recall.

in this stage, we simply link every pair of mentions together if there is any links can link them together to generate the initial coreference chain.


Instead, every CoNLL paper is allotted a 15 minute oral presentation slot as well as a poster.


We adopt this idea to generate two views of the instances in both domains by randomly splitting the common feature space into two disjoint feature subsets, such that t,r }.

the initial prediction model will include two predictors (f (1) , f (2) ) with model parameters (w , each trained on one view of the labeled data using the perceptron algorithm.


Our intuition is that one should query the less expensive source domain more frequently.

more labeled instances can be collected within the fix budget.


Solving for a 1 in terms of a −1 we have a 1 = p 1−p a −1 .


we have  Table 8: Values of the scaling factor from Theorem 3.5 for different precision values.


To use the information from this graph to inform our CRF, we can use the graph Laplacian: where D is a diagonal degree matrix with The larger the disparity in v values of similar vertices, the larger the value of v Lv.

the matrix L is positive semi-definite, so v Lv is convex in v. If each entry v a is a linear function of the vector of marginals m described above, then v(m) Lv(m) is convex in m. for any linear v(m), we can use this Laplacian expression as a PR penalty.


Applying EGD, we know that we can efficiently find the E-step optimum.

the EGDbased EM algorithm efficiently implements coordinate ascent onJ (θ, q), with each step monotonically increasingJ : Hence, we have shown that it is possible to efficiently use an arbitrary convex, differentiable function of the marginals, h(m), as a PR penalty function.


Finally, we update the weight vector W t trained with boosting as follows: This process is repeated T times, and a model W T , which consists of T types of Semi-PERbased models, is obtained.

in test phase, the segments and labels of a word sequence X is decided as follows: Since our algorithm handles real valued scores of samples given by Semi-PER on the exponential loss of AdaBoost, it's difficult to analytically determine a confidence-value α t that satisfies Z t (α t ) < 1 at boosting round t. we use a bisection search to find a confidence-value.


The results indicate there is a significant difference (p <   We compared training speed under the following condition; The iteration for Semi-PER is 100, the iteration number for Semi-CRF trained with SGD is 100×m, where m is the number of training samples, and the one time iteration of boosting with the perceptron iteration 100.

all training methods attempted 100 × m times estimation.


For a first reading, note that an R-HMM sequence can be seen as a right-branching L-PCFG tree.

in principle, one can convert a sequence into a tree and run the inside-outside algorithm of Cohen et al.


A recent analysis of approaches to evaluating compression (Napoles et al., 2011b) has shown a strong correlation between the compression rate and human judgments of compression quality, thereby concluding that comparisons of systems which compress at different rates are unreliable.

all comparisons that we carry out here involve a restriction to a particular compression rate to ensure that observed differences can be interpreted meaningfully.


In particular, we use PTK on the relational tree structures combined with the polynomial kernel of degree 3 applied to the feature vectors.

different representations lead to different models described below.


2, that during the first iterations of EM, the sparse vectors obtained with the ε-best projection have a large number of non-zero elements.

this projection is not adequate to directly learn large latent class models.


However, common to most existing work, words are regarded as independent entities without any explicit relationship among morphologically related words being modeled.

rare and complex words are often poorly estimated, and all unknown words are represented in a rather crude way using only one or a few vectors.


The main advantage of having such a distributed representation over word classes is that it can capture various dimensions of both semantic and syntactic information in a vector where each dimension corresponds to a latent feature of the word.

a distributed representation is compact, less susceptible to data sparsity, and can implicitly represent an exponential number of word clusters.


The NLMs, on the other hand, utilize surrounding word contexts to provide further semantics to the learned morphemic representations.

our context-sensitive morphoRNN embeddings could significantly outperform existing embeddings on word similarity tasks for many datasets.


Up till now, however, most of the evaluation has been done on monolithic corpora such as the Penn Treebank, the Proposition Bank.

it is still unclear how the state-of-the-art analyzers perform in general on data from a variety of genres or domains.


The amount of data we can get together in such a test set is big enough to be representative.

we decided that it would be ideal to choose a portion of these documents as the test collection for all layers.


Unfortunately, in the case of Chinese and Arabic, either the historical partitions were not in the selection used for OntoNotes, or were partially overlapping with the ones created using this scheme, and/or had a very small portion of OntoNotes covered in the test set.

we decided to create a fresh partition for the Chinese and Arabic data.


Due to monotonicity, there is a natural pairing between the Right-Arc and Reduce actions and the Shift and Left-Arc actions: a word which is pushed into the stack by Right-Arc must be popped using Reduce, and a word which is pushed by Shift action must be popped using Left-Arc.

of this pairing, a Right-Arc move determines that the head of the pushed token must be to its left, while a Shift moves determines a head to its right.


However, it is often difficult to diagnose a sampler's convergence, and mixing is notoriously slow for distributions with tightly coupled hidden variables such as PCFGs, especially when the data sets are large.

there remains a challenge for more efficient, but also accurate and deterministic inference algorithms for PCFGs.


where is the inside probability of observation w i,k = w i , ..., w k given A is the root of the subtree, and P OUT (A, i, k) is the probability of A spanning (i, k), together with the rest of w. In the M step, we find the optimal θ based on the MLE principle: VB inference is the generalisation of EM in the sense that it allows arbitrary parametric forms of Q(θ).

the update equation in the M step is: By the conjugacy property, the new Q(θ) is still in Dirichlet distribution form except with updated hyperparameters as shown by Kurihara and Sato (2006).


(2007) proposed a Hastings sampler that specified an alternative rule probabilities θ H of a proposal distribution P (t i |w i , θ H ), where The rule probabilities θ H are based on the statistics collected from all other parse trees, and they are fixed for the conditional distribution of the current parse tree.

by using a variant of inside algorithm (Goodman, 1998), one can efficiently sample a parse tree, which will be either accepted or rejected based on the Metropolis choice.


Hence, the mean field assumption is better satisfied in collapsed space with very weak dependencies than in joint space with strong dependencies.

we expect that VB in collapsed space can achieve more accurate results than the standard VB, and the results would be very close to the true posterior.


at the tree level, a PCFG can be considered as an i.i.d.


We keep track of the expected counts at global level, subtract the local mean counts for t i before update, run the inside-outside recursion using θ CVB , and finally add the updated distribution back into the global counts.

we only need to replace the parameters with the expected counts, and make update after each sentence; the core of the insideoutside implementation remains the same.


: However, it is not obvious how to use this alternative approach in general, and the performances of resulting algorithms remain unclear.

we implement only the CVB algorithm at the tree level in Section 3.2 for our experiments.


While not state-of-the-art, the models we have demonstrated our CVB algorithm on underlie a number of high performance grammar induction and parsing systems (Cohen and Smith, 2009;Blunsom and Cohn, 2010;Liang et al., 2007).

our work naturally extends to employing our CVB algorithm in more advanced models such as hierarchical splitting and merging system used in Berkeley parser , and generalising our CVB algorithm to the non-parametric models such as tree substitution grammars (Blunsom and Cohn, 2010) and infinite PCFGs (Liang et al., 2007).


To calculate the phrase score, a linear combination of the hidden layer activations A is computed using W 2 and b 2 .

the five parameters that have to be learned are   We have chosen to generate our word embeddings from Wikipedia.


In our case, we recognize the input utterance by using an Automatic Speech Recognizer (ASR) in the source language, and we then translate the hypotheses provided by the ASR into the target language t by means of a statistical Machine Translation system (see Figure 1).

by considering both the input sentence W s uttered by the user and its translation into the target language W t , Equation (1) can be rewritten as: Equation (2) can be decomposed into several factors, as shown in Equation (3).


However, due to the difficulty of the problem, this modelization may not be enough.

we can not guarantee that the best translation obtained by the model is consistent with the meaning of the original sentence.


This graph has the same set of nodes as the graph of words, but each arc represents that there is a path in the graph of words between the initial and ending node of the arc, which induces a sequence of words that is relevant to some of the concepts of the task.

each of these arcs is labeled with the corresponding sequence of words and the concept they represent.


As in many other dialog systems (Minker, 1999), the semantic representation chosen for the task is based on a frame representation.

the final output of the understanding process is one or more frames with their corresponding attributes.


For evaluation purposes, all the test set was manually translated into French, and 500 turns were uttered by four native French speakers.

we have carried out experiments both considering as the input to our system the correct sentences in French (which is the same than assuming a perfect ASR) and the utterances.


when the input phrase is not found in the bilingual corpora.

we assess precision (P) and recall (R) as the number of paraphrases in context that are judged correct out of the number of cases for which a candidate paraphrase was proposed, and out of the total number of test samples, respectively.


), leading to a vocabulary-document co-occurrence matrix with sparse block-diagonal structure.

there may be enough statistics train IBM-1 on documents.


Note that this feature does not incorporate any potential level of difficulty in processing an infinitival vs. finite SC; the feature simply records that there are three different types of embedded arguments: SC-inf, SC-fin, or none.

while Desire and Belief verbs that typically occur with an SC-inf or SC-fin have a distinguishing feature, there is nothing in this representation that makes Desire verbs inherently easier to process.


Moreover, the possibility in the model for "misinterpretation" of mental content as action semantics (see Section 2.3) also applies equally to both types of verbs.

any observed performance gap in the model reflects an interaction between its processing approach and the distributional properties of CDS.


The HOO shared task of 2012 is aimed at detecting and correcting misuse of determiners and prepositions, two types of errors accounting for only 38% of all errors.

there are a lot more errors related to other parts of speech that we have to address in this year's shared task.


The proposed solution is based on the hypothesis that the usage of preposition often depends on the collocation relation of verb or noun.

we propose a back-off model, which utilizes the dependency relations to identify the related words of the preposition in question.


However, the verb "is", which is immediately after "purpose", has a strong influence to keep the word in singular form.

it may be better not to use a verb that the word is not immediately dependent on as a feature.


With the increasing number of people all over the world who study English as their second language 1 , grammatical errors in writing often occurs due to cultural diversity, language habits, education background, etc.

there is a substantial and increasing need of using computer 1 A well-known fact is that the most popular language chosen as a first foreign language is English.


Different types of corrections may interact with each other.

only for each focus word in a sentence will pass the filter and predict by the system.


While developing our system, we found that adding more prepositions did not increase performance in our experiments.

the confusion set is {in, for, to, of, on, ø}.


After the first level filtering by the rules, the rest of noun phrases are indeterminacy by system.

we use a ME classifier for further filtering.


To detect the tense of verb error is much more related to the semantics level than syntax level.

it is hard to extract a common feature for training model.


In many cases, our system makes good corrections which are not considered to belong to any of the target error types, as illustrated in the following example.

we should not compare now with the past but we need to worried about the future problems that caused by this situation.


First, there is no clear-cut definition of each error type, so it is not possible to know the annotation criteria or scope of each error type.

inferring this information from the annotated examples may result in poor error mapping between the CLC and NUCLE, making the system learn corrections that are not part of our target set and miss others which are actually useful.


The difficulty of the problem lies in the fact that in order to adequately assign an article or determiner to the noun, it is not sufficient to rely only on the sentence.

is also necessary to go beyond the sentence and move on the level of text or discourse, which requires natural language processing techniques that we currently lack but are highly needed.


A new token can only be added before or after an existing token.

for labels with addition, we must find some token that the label can be assigned to.


Figure 3: Different thresholds of LM filters With higher threshold, more correction with lower information gain will be obtained.

the recall grows higher but the precission grows lower.


The number of the noun, the choice of determiner and verb's agreement in number with the subject are clearly inter-related.

a coordinated approach is necessary to correct these errors.


The choice of determiners, especially articles, depends on many factors including lexical, syntactic, semantic and discourse phenomena (Han et al., 2006).

the correct usage of determiners is difficult to master for second language learners, who may (i) insert a determiner where it is not required, (ii) omit a required determiner, or (iii) use the wrong determiner.


In the training set, the number of instances with no corrections far exceeds the number of instances with corrections.

a balanced training set was created by including all the instances with corrections and sampling α instances with no corrections from the training set.


Each NP that is extracted represents one training example.

if an NP is incorrect then we label it to one of the label from Table 2.


In choosing our confusion sets, we wanted to be flexible and extensible.

we did not want to depend on corpora of errors with annotated corrections to infer alternatives.


As explained in the introduction, one goal of our system is to allow flexible confusion sets that do not need to be fully specified in advance of learning statistics about them.

we define our confusion sets in terms of the standard POS tagsets as given by the Stanford tagger, using a notion of closed vs. open word classes.


Because our closed classes each include the empty string, every empty string in the observed sentence could have been produced by the deletion of a member of any of the closed classes.

we also consider the possibility of inserting each word x, from each closed class.


However, automated grammar error correction is a very difficult field and the result is not satisfactory.

the shared task about English error correction has been annually held and many researchers are trying to solve this problem.


Table 4 shows that the feature name addition does not improve the precision in the case of article correction, and the set of 18 features achieves the best performance for article correction.

we just use raw words for features and select 18 features for article correction.


If the sample is very small, though, this is likely not the case.

with small sample sizes, there is a risk that the calculated p-value will be artificially low-simply because the bootstrap samples are too similar.


For each descriptive word, we estimate P (w i |q img ), using topic proportions estimated using Equation 6: This is used to find I(w i ), a function of the likelihood of each word in the extracted caption: This function considers the prior probability of w i because frequent words often have a high posterior probability even when they are inaccurate.

the sum n i=1 δ i • I(w i ) is the overall measure of the correctness of a proposed caption conditioned on q img .


Crucially, Arabizi is not a simple transliteration of Arabic, under which each Arabic letter in some orthography is replaced by a Latin letter (as is the case in the Buckwalter transliteration used widely in natural language processing but nowhere else).

it is not straightforward to convert Arabizi to Arabic.


The probability of any path in the A* search space combines the FST probability of the words with the probability from the language model.

for any certain path of selected Arabic possibilities A 0,i = {a 0 , a 1 , ...a i } given the corresponding input Arabizi sequence W 0,i = {w 0 , w 1 , ...w i }, the transliteration probability can be defined by equation 1.


We instead verified the hypothesis that a search engine could achieve a better result.

we opted for indexing the DB clues and their answers with the open source search engine Lucene (McCandless et al., 2010), using the state-of-the-art BM25 retrieval model.


We stress the fact that this set of clues is disjoint from the clues in the training and test sets.

identical clues are not present across sets.


While pre-segmentation of the discourse means we can avoid aligning internal constituents of trees, we still need to treat every utterance as an alignment candidate, without a sparse combinatorial constraint.

the sum over A and C is no longer tractable to compute explicitly, and approximate inference will be necessary.


Since most weighting schemes reward high relative co-occurrence, these features are also likely to receive high weights.

we suggest that features which are not found for both terms should have a decreased impact on the score calculation, as many of them are not expected to be shared between hyponyms and hypernyms.


These are properties that co-occur often with the term f ood, but can also be applied to most hyponyms of food.

we hypothesise that the presence of these features for the narrower term is a good indication of a hyponymy relation.


The absence of a constraint in T1 or T3 is treated as a value of −∞ and the absence of a constraint in T2 or T4 is treated as +∞, which lead to zero-value terms in the scoring sum.

the overall achievable score has a range between 0 and 1.


In such cases, there are a large number of possible labels for each token, so picking the right one simply by chance is unlikely; the parameter space tends to be large; and devising good initial parameters is difficult.

it is unsurprising that the unsupervised (or even weaklysupervised) learning of a Combinatory Categorial Grammar (CCG) supertagger, which labels each word with one of a large (possibly unbounded) number of structured categories called supertags, is a considerable challenge.


Because they are recursively defined, there is an infinite number of potential CCG categories (though in practice it is limited by the number of actual grammatical contexts).

the number of supertags appearing in a corpus far exceeds the number of POS tags (see Table 1).


However, when considering large joint-contexts for c, this approach becomes similar to the composite-feature approach since it is based on co-occurrence counts of target words with large joint-contexts.

we expect in this case to encounter the data sparseness problems mentioned in Section 1, where semantically similar word type pairs that share only few or no identical joint-contexts yield very low p sim (b|a) estimates.


Nevertheless, we expect our approximation p sim θ (b|a) to converge with a reasonable sample size from a's joint-contexts.

in order to bound computational complexity, we limit the size of the context collections used to train our model to a maximum of N by randomly sampling N entries from larger collections.


First, the performance of our generic scheme is largely inherited from the nature of its underlying language model.

we see much potential in exploring the use of other types of language models, such as class-based (Brown et al., 1992), syntax-based (Pauls and Klein, 2012) or hybrid (Tan et al., 2012).


The model parameters, namely the dimension of the semantic space and the number of utterances to be taken as defining the context of a given word form, are set in an unsupervised way to optimize the latent structure of the semantic model .

we use 20 utterances as a semantic window and set the semantic space to 100 dimensions.


The latter creates alternants of the same lexical item since adjacent sounds condition the realization of the first and final phoneme.

finding a minimal pair of words differing in the first or last segment (as in [kanaX] and [kanaK]) is good evidence that these two phones ([K], [X]) are allophones of one another.


The first prompt was also used in the NUCLE training data, but the second prompt is entirely new and not used previously.


Our rule description system is inspired after the time-independent logic function (combinational logic) paradigm, which stipulates that a fixed input size logical function, described through a stochastic list of input/output dependence sequence, through a process of logical minimization, this function can be implemented as an array of "AND" gates, followed by an array of "OR" gates.

in our configuration file, each rule is described by a set of string pairs (i 0 r 0 , i 1 r 1 … i n r n ) which act as "AND" gateswe refer to this as a sub-instance of a rule.


Most recent work (Cahill et al., 2013;Rozovskaya & Roth, 2011; has used machine learning methods that rely on a GEtagged corpus such as NUCLE, Japanese English Learner corpus, and Cambridge Learner Corpus (Dahlmeier et al., 2013;Izumi et al., 2005;Nicholls, 2003), because well-formed and GEtagged approaches are closely related to each other, can be synergistically combined.

research using both types of data has also been conducted (Dahlmeier & Ng, 2011).


However, using only one best result is not very accurate.

among the best results in the Enchant Python Library, we select the one best word, i.e.


Because the count of "years ago where" is greater than that of "years back where", the former is selected as the correct form.

the sentence "This ability is not seen 40 years back where the technology advances were not as good as now."


In non-native text, parsing and tagging errors are inevitable, and it may cause false alarm.

instead of dependency parsing to find subject and verb, we consider the preceding five words because erroneous sentences often contain dependency errors.


However, for CoNLL-2014, all 28 error types are required to be corrected and some of these types such as Rloc-(Local redundancy) and Um (Unclear meaning) are quite complex that the manual decomposition is time consuming and requires lots of grammatical knowledges.

an automatica decomposition method is proposed.


A new token can only be added before or after an existing token.

for edit operation with addition, we must find an existing token that the label can be assigned to, and this sort of token is defined as pivot.


One possible reason is that few errors (in the training corpus) involve word prefixes.

the prefix does not seem to be a suitable factor for tackling the GEC problem.


In this work, we correct the two types of errors based on the assumption that the usage of preposition often depends on the collocation relations with a verb or noun.

we use the dependency relations such as dobj and pobj, and prep to identify the related words, and then we validate the usage of prepositions, and correct the preposition errors.


Some of the errors are related to the wrong deletions or insertions.

we choose to take special care of determiners and prepositions to correct all their replacement, deletion and insertion errors instead of generating candidates for them in this stage.


We consider whether the space should keep empty, or insert 'a' or 'the'.

3 labels are considered to indicate 'a', 'the' and 'NULL'.


We assume that there exists a derivationh t for the feedback structureȳ t , such that for all predictions y t , the (re-scaled) utility of the weak feedbackȳ t is higher than the (re-scaled) utility of the prediction y t by a fraction α of the maximum possible utility range (under the given utility model).

∀t, ∃h t , ∀h and for α ∈ (0, 1]: where ξ t ≥ 0 are slack variables allowing for violations of (2) for given α.


The inference algorithm is inspired by the best-left-link approach , where they solve the following ILP problem: Here, y u,v = 1 iff mentions u, v are directly linked.

we can construct a forest and the mentions in the same connected component (i.e., in the same tree) are co-referred.


The ACE-2004 dataset is annotated with both mention and mention heads, while the OntoNotes-5.0 dataset only has mention annotations.

we preprocess Ontonote-5.0 to derive mention heads using Collins head rules (Collins, 1999) with gold constituency parsing information and gold named entity information.


The Bayesian framework is well-matched to our problem since our inductive biases -those derived from universal grammar principles, weak supervision, and estimations based on unannotated data -can be encoded as priors, and we can use Markov chain Monte Carlo (MCMC) inference procedures to automatically blend these biases with unannotated text that reflects the way language is actually used "in the wild".

we learn context information based on statistics in the data like CCM, but have the advantage of additional, a priori biases.


Assuming T is the full set of known categories, the generative process for our model is: The lazy dog sleeps n Figure 2: Higher-level category n subsumes the categories of its constituents.

n should have a strong prior on combinability with its adjacent supertags np/n and s\np.


However, the results in Figure 1 suggest that it is possible to manipulate verbosity by controlling the average source sentence length of the dataset (and the source-side length is always known for any test set).

in our study, we use the source-side sentence length as a data selection criterion; still, we also report results for selection based on verbosity.


Moreover, our previous findings suggest that for PRO, higherverbosity tuning datasets will perform better in this situation.

we should expect that longer tuning datasets could yield better BLEU.


For MWE, BROWN performs slightly better than the other methods when trained with approximately 25% of the training instances.

we conjecture that the POS tagging and NER tasks benefit more from distributional similarity than Chunking and MWE.


Decreased error rates of this type correlate most strongly with obligatory pronouns in subject position, as well as a verbal person marking system similar to the one in English.

far, we presupposed the availability of substantial typological information for our target languages in order to predict their ESL error distributions.


First, the limited length of the posts compels the users to adopt a terse style that leaves out many grammatical markers.

performance of syntactic parsers is prohibitive for linguistic analysis in this domain.


three-token structures can subsume smaller ones).

small and inessential variations in the dataset can determine which of the variables are selected to represent the group, so we end up with random within-group feature selection.


Detecting semantically equivalent questions is a very difficult task due to two main factors: (1) the same question can be rephrased in many different ways; and (2) two questions could be asking different things but look for the same solution.

traditional similarity measures based on word overlap such as shingling and Jaccard coefficient (Broder, 1997) and its variations (Wu et al., 2011) are not able to capture many cases of semantic equivalence.


Given a question consisting of N words q = {w 1 , w 2 , ..., w N }, every word w n is converted into a real-valued vector r wn .

for each question, the input to the next NN layer is a sequence of real-valued vectors q emb = {r w 1 , r w 2 , ..., r w N } Word representations are encoded by column vectors in an embedding matrix W 0 ∈ R d×|V | , where V is a fixed-sized vocabulary.


In addition, the generative model ("Generative INN, large beam" in Table 1) was compared extensively to state-ofart parsers on various languages and tasks in previous work (Titov and Henderson, 2007b;Titov and Henderson, 2007a;Henderson et al., 2008).

here our objective is not repeating an extensive comparison to the available parsers.


The x-axis corresponds to different values for the filtering threshold.

when increasing the threshold, the number of sentences used for training decreases.


The highest performance is achieved with the full set of training instances.

it may be concluded that for aspect name detection, high quality training is crucial.


Although plenty of text categorization datasets have been annotated, common practice reflects that initial noisy annotations be discarded and only consensus labels be published.

we follow previous work in achieving broad validation by constructing synthetic annotators that corrupt known gold standard labels.


To alleviate this problem, we design a mutuallearning regime to predict representations of unknown words for each embedding version by learning projections between versions.

all embedding versions have the same vocabulary.


Each type has a characteristic mapping between semantic roles and opinion holders and targets.

the problem of opinion role induction is reduced to automatically categorizing opinion verbs.


We also evaluate our approach in the context of cross-domain opinion holder extraction.

we demonstrate the importance of our approach in the context of previous datasets and classifiers.


We assume that it is lexical information that decides what semantic role an opinion holder or opinion target takes.

we built a gold-standard lexicon for verbs that encodes such information.


Rather than induce the opinion roles for individual verbs, we group verbs that share similar opinion role subcategorization.

the main task for induction is to decide which type an opinion verb belongs to.


It is the product of the minimum probability that any two objects in the positive examples are in the same set multiplied by the complement of the maximum probability that any two objects across the partition are in the same class.

for partition N with positive examples + and negative examples −: The classifier probability is similar, except rather than comparing objects to other objects in the partition, the objects are compared to the mean of the column's descriptor C in the descriptor's representative feature.


An edge is considered overspecified if the hypothesis for the objects that satisfy the next descriptor does not add additional information, i.e., the set-intersection it corresponds to does not remove any possible objects from the referent set.

the model will prefer hypotheses for the next descriptor that narrow down the hypothesized set of referents.


While these datasets have been annotated with traceability links, they are not annotated with annotator rationales.

we employed a software engineer specializing in requirements traceability to perform rationale annotation.


A learner that successfully learns this difference in confidence assigns a higher importance to the pieces of text that are present only in the original instances.

the pseudo-instances help the learner both by indicating which parts of the documents are important and by increasing the number of training instances.


NPs in object position take an article 75% of time while NPs in subject position take an article 96% of time.

nPs without articles and on the left of the head are observed only 135 times in our data sample (across all languages).


This relation can be accurately identified using patterns such as "X such as Y" and "X like Y" (Hearst, 1992).

it is likely that a pattern-based model can be adapted to control its predictions with respect to this relation using a method similar to the one we use to control antonym representation.


For example, the similarity score of the (life,death) and (profit,loss) pairs are 7.88 and 7.63 respectively, on a 0-10 scale.

we turn the antonym parameter off for this experiment.


for ordered relations and one class (Other) for other relations.

the task can be treated as a 19class classification task.


Please refer to the examples in the 11th sentence of the 2nd training document of the TempEval-2 Korean dataset.

we constructed a new dataset called Korean TimeBank.


The tlink extractor makes use of the predicted makeinstance tags and predicted timex3 tags.

the performance of timex3 extractor and event extractor will strongly influence the performance of makeinstance extractor and tlink extractor.


The other relTypes follow the same definition of TimeML.

it is essentially a classification over 11 classes for each pair of two argument tags.


Since transition-based parsers run in linear time, our aim is to speed up spinal parsing while taking advantage of the rich representation it provides.

the research question underlying this paper is whether we can accurately learn to take greedy parsing decisions for rich but complex structures such as spinal trees.


A spinal dependency is a tuple h, d, p that represents a directed dependency from the p-th node of σ h to the d-th node of V .

a spinal dependency is a regular dependency between a head token h and a dependent token d augmented with a position p in the head spine.


The arc-eager transition system is not guaranteed to generate a single root in a derivation (though see (Nivre and Fernández-González, 2014) for a solution).

after mapping a derivation to a spinal structure, we might get a forest of projective spinal trees.


Proposed w/o surface uses only the training data to find translatable context pairs by setting β sim = 0.

its advantage over Direct Mapping confirms the importance of learning a translation matrix.


The typical way of training NLMs is to maximize the training data likelihood, or equivalently, to minimize the cross-entropy objective of the following form: Training NLMs can be prohibitively slow due to the computationally expensive softmax layer.

past works have tried to use a more efficient version of the softmax such as the hierarchical softmax (Morin, 2005;Mnih and Hinton, 2007;Mnih and Hinton, 2009) or the classbased one (Mikolov et al., 2010;Mikolov et al., 2011).


According to series of recent publications in Bulgarian media, Russian Internet users reportedly use the term murzilka to refer to Internet trolls.

this term was adopted by some pro-Western Bulgarian forum users as a way to refer to users that they perceive as pro-Russian opinion manipulation trolls.


Additionally, the average length of an English tweet is 10-12 words whereas the average length of a document in Reuters-21578 news classification corpus is 200 words.

the dimensionality is extremely high even for small corpora with short texts.


More the difference, better the discriminative power of the coefficient.

discriminative features tend to have a higher average probability density in one of the classes whereas redundant features cancel out in taking the difference in computing the distance.


The method is based on adaptive discriminant wavelet packet transform (ADWPT) (Qureshi et al., 2008).

we name it as improvised adaptive discriminant wavelet packet transform (IADWPT).


Our method does not depend on a specific classifier used in the final classification.

we used   (Cortes and Vapnik, 1995) and Logistic Regression with and without dimensionality reduction for unigram representation to benchmark the performance.


The TBSG model aims to learn matrices that act in a compositional manner.

a more interesting evaluation of its performance is to test how well the matrices combine with noun vectors.


But we didn't want NLL to be seen as just another application of Machine Learning, particularly as we (and our sister SIG-DAT) developed increasingly large and expensive resources, including tagged corpora.

we moved away from the name "Machine Learning of Natural Language" which was the title of the book based on my own PhD thesis (Powers and Turk, 1989) while the AAAI event I organized added "and Ontology" (Powers, 1991a).


Successful features include timexes, specific temporal (past, present, future) words from a commercial dictionary, but also ngrams, thus indicating that temporality may be embodied by multi-word terms, whose temporal orientation is unknown.

discovering the temporal orientation of words is a challenging issue that may benefit many text applications.


Different weights can be assigned to different relations to reflect the degree to which they preserve temporality.

we adopt two strategies to assign weights to different WordNet relations.


A synset belongs to a majority class k if the most frequent annotation for the synset was selected by at least k annotators.

a large percentage of synsets belonging to high majority classes are symptomatic of good inter-annotator agreement.


Concretely, we extend the objective in the classical unsupervised k-means algorithm by adding a penalty term from labeled data.

the new objective covers three key groups of parameters: centroids of clusters, the cluster assignment for each text, and the parameters within deep neural networks.


To deal with variable feature size, we perform a maxpooling operation over all the features to select the maximum value.

after the two operations, each filter generates only one feature.


we only need to minimize the first term by assigning each text to its nearest cluster centroid, which is identical to the E-step in the k-means algorithm.


We found that 100 is the best output dimension for both CNN and LSTM models.

we set the output dimension as 100 in the following experiments.


we choose α=0.01 in alpha the following experiments.


For example, going to a restaurant involves entering the restaurant, getting seated, making an order and so on.

when describing a visit to a restaurant, a writer will not specify all the events, as they are obvious to the reader.


To allow the classifier to learn, we must encode the interactions between the verb features and the context features.

we use the Cartesian product of sentence and verb features to encode interactions between them: for each training sentence we generate both a positive and a negative example.


We produced a version of BNC where all the words were replaced with their lemmas and PoStags converted into the Universal Part-of-Speech Tagset (Petrov et al., 2012) 1 .

each token was represented as a concatenation of its lemma and PoS tag (for example, 'love_VERB' and 'love_NOUN' yield different word types).


This is a significant improvement over the onefeature baseline classifier (classify using only one vector dimension with maximum F-value in relation to class tags), with F-score equal to only 0.22.

the results support the hypothesis that word embeddings contain information that allows us to group words together based on their parts of speech.


The Universal Dependencies Treebank is more consistent in this respect: it separates entities like '1$' into two tokens: a numeral (NUM) and a symbol (SYM).

when our classifier was tested on the words from the UD Treebank, there was only one occurrence of this type of error.


Sometimes these tokens are tagged as ambiguous, and the adjective tag is there as a second variant; however, the corpus documen-tation states that in such cases the first variant is always more likely.

distributional models can actually detect outright errors in PoS-tagged corpora, when incorrectly tagged words strongly tend to cluster with another part of speech.


It is also interesting that one needs as few as a thousand example words to train a decent classifier.

it seems that PoS affiliation is expressed quite strongly and robustly in word embeddings.


Note that the same partition by triples was used across all tasks.

a German noun/English noun pair that appears in test data is genuinely unseen-it did not appear in the training data for any task.


A language-learning interface should display an L2 word only when the user has a good chance of guessing its L1 translation.

we also assess our features and model on the easier task of predicting the guessability of a task instance x-that is, the average empirical accuracy of our subjects on this instance, meaning the fraction of the 9 subjects whose guessŷ exactly matched the reference English translation y * .


The most refined form of MDL, NML-Normalized Maximum Likelihood, (Rissanen, 1996)-cannot be efficiently computed for our model.

we resort to a classic two-part coding scheme.


F is one of the possible features found at that position.

we have in total about 2 levels × 8 positions × 5 features ≈ 80 candidate contexts that can be queried, as explained in detail below.


This feature engineering is labor intensive, and depends on external tools and resources.

deploying such systems in practice is expensive, time-consuming and unwieldy.


The intuition is that in the aggregate, such words are less likely to be employed by user j than words observed in sentences she has authored.

minimizing this objective attempts to induce a representation that is discriminative with respect to word usage.


An example of POS projection via word alignments between Malagasy and English is shown in word in English or the NULL word.

there exist words in the target language which are not aligned to a word in the source language, for example ny in Figure 1.


s |s| and some user feedback f , we must find the best target language trans- We can make the naïve Bayes' assumption that s and f are statistically independent variables given t. This results in the basic equation for ITP with error correction (Ortiz-Martínez, 2011): where, as we will see in Section 3.2, distribution Pr(t | s) can be approximated by a machine translation model, and Pr(f | t) by an error correction model that measures the degree of compatibility between f and t. Note that by using a probability distribution Pr(f | t), any translation is compatible with a given user feedback to some degree.

the translation returned by Equation (1) may still not contain the segments validated by the user; we need to identify the sub-string of the returned sentence that corresponds to the each of the segments validated by the user.


In addition to be an MT quality metric, TER can also be seen as a human-effort measure in PE scenarios.

we can use TER and WSR to compare human effort between PE and ITP.


We introduce a language independent method for NER, building on cross-lingual wikification, a technique that grounds words and phrases in non-English text into English Wikipedia entries.

mentions in any language can be described using a set of categories and FreeBase types, yielding, as we show, strong language-independent features.


One issue with all the aforementioned studies is that inaccuracies often cause cascading errors in the pipeline (Zheng et al., 2013).

most authors concentrate on high-precision linking at the cost of low recall.


It turns out that, for CR systems, mentions of the MC often are spread over several coreference chains.

we devised two more baselines that aggregate chains, with an expected increase in recall.


position Often, pronouns at the beginning of a new section or paragraph refer to the main concept.

we compute 5 (binary) features encoding the relative position (first, first tier, second tier, last tier, last) of a mention in the sentence, paragraph, section and article.


Their approach involves randomforest classification with best-link clustering and propagation of attributes between event mentions, and is grounded on a manifold of external feature sources, i.e., it uses a "rich set of 105 semantic features".

their approach is strongly tied to domains where these semantic features are available and is potentially hard to port to other text kinds.


Inspired by WLM, the KB graph model simply learns to place entities with similar incoming links near one another in the vector space.

we formalize this as the following objective function: we compute the conditional probability P (e o |e i ) using the following softmax function: we train the model to predict the incoming links C e given an entity e. C e plays a similar role to context words in the skip-gram model.


the objective function is transformed from that in Eq.


The total number of words and entities in the embedding were approximately 2.1 million and 5 million, respectively.

the number of rows of matrices V and U were 7.1 million.


We trained our model using the training set and assessed its performance using the test set.

we evaluated our model on 1,020 mentions contained in the test set.


Across languages, the best substring-based models improve top-1 accuracy by upto 11% and top-10 accuracy by upto 25% over the best characterbased models.

it is clear that contextual information can be harnessed in an unsupervised setting to substantially improve transliteration accuracy.


Unlike Hindi, Kannada assumes an implicit vowel 'A' at the end of a word unless another vowel or nasal character terminates the word.

the vowel suppressor character (halanta) must be generated during hi→kn transliteration.


Full-length F1 solves this problem since it can penalize longer summaries.

we use full-length F1 scores from 1, 2 and L variants of Rouge using the official script to evaluate our systems.


Convergence of all models is also slower on this dataset compared to Gigaword, taking nearly 35 epochs for all models.

the wall-clock time for training until convergence is about 7 days for the flat models, but nearly 18 days for the hierarchical attention model.


A confident prediction means the parser will assign a high probability to the one output sense.

we use the negative surprisal of the estimated probability P p of the parser output sense s output (Equation 14) to approximate I(s; arg, C).


Multi-sense is an important factor of our DC production model: a speaker could have chosen an explicit DC for each sense, but if s/he has to express two senses at the same time, an implicit DC could be more usable.

we treat all combination of senses as individual senses, each containing 1 to 3 joint sense labels 13 This results in a total of 122 senses.


However, manually creating training data to match the test domain is not a preferred solution, because 1) sometimes the test domain is not known when training the model, and it could change from sentence to sentence; 2) even if the test domain is pre-determined, the resources required and slow turnaround in data collection process will still delay the system development process.

training data selection is widely used for domain adaptation in statistical machine translation (Zhao et al., 2004;Lü et al., 2007;Yasuda et al., 2008;Moore and Lewis, 2010;Axelrod et al., 2011;Duh et al., 2013;Axelrod et al., 2015).


When the in-domain sample is reduced to 100 sentence pairs, the LM-based methods only get a small improvement over the baseline, while the "ohcnn" got a 1.2 BLEU score improvement over the baseline and "sscnn" got a 2.1 BLEU improvement over the baseline.

even if we have no domain knowledge about the training data, when we have only 100 sentences in the test domain, the semi-supervised CNN classifier can still select a good in-domain subset and achieve good performance.


Furthermore, the graph decomposition procedure is utilized to generate training data for building sub-models.

we hope each subgraph y k is informative enough to train a good disambiguation model.


Based on the properties of water, the verb emphasizes dynamic flows.

the verb tends to appear in non-passive constructions for expressing the dynamics.


Our goal is to increase the precision of metaphor detection with the aid of the semantic classification of radicals.

we conduct word embedding to show how different concepts are categorized in terms of their semantic similarities.


Event types correspond to larger conceptual categories.

verbs of the same group have similar syntactic distribution.


In both cases, we propose task-specific solutions.

we start with the all-singleton partition, where each mention makes its own entity and then try to incrementally grow our entities.


at the merge iteration i, the system observes n−i entities it has already seen before and one new entity generated at the merge iteration i − 1.


The final partition depends on the similarity measure used by the collaborative algorithm.

the MELA measure, being an average of   MUC, B 3 and CEAF, leads to more balanced final partitions, improving on each individual score.


(4) Based on the probabilistic model above, the loss function can be defined as the difference between the distribution over the ranking according to eval(•) and s(•).

we introduce the following two standard listwise losses.


Therefore, we think that getting the ranking right at the top of a list is more relevant for tuning.

we should pay more attention to the top-ranked translations instead of forcing the model to rank the entire list correctly.


The leading principle is that, since a word is the surface form of an underlying sense, updating the embedding of the word should produce a consequent update to the embedding representing that particular sense, and vice-versa.

of the algorithm described in the previous section, each word in the corpus may be connected with zero, one or more senses.


The MCS baseline is generally integrated into the pipeline of stateof-the-art WSD and Entity Linking systems as a back-off strategy (Navigli, 2009;Jin et al., 2009;Zhong and Ng, 2010;Moro et al., 2014;Raganato et al., 2017) and is used in various NLP applications (Bennett et al., 2016).

a system which automatically identifies the MCS of words from non-annotated text may be quite valuable, especially for resource-poor languages or large knowledge resources for which obtaining senseannotated corpora is extremely expensive.


If both are used, the label is simply conj=conjlr+conjll.

7 the individual context bags we use in all experiments are: subj, obj, comp, nummod, appos, nmod, acl, amod, prep, adv, compound, conjlr, conjll.


Unlike in natural language corpora, the sources are independent: e.g., dog never directly appears with cat, and hence any learned relation between them could not be due to first-order information.

by defining crisp semantic categories and sentence frames, we investigate how first and second-order co-occurrence information sources are consumed and represented in terms of similarity between words by count-based and predictive DSMs.


One should consider that while dimensionality reduction to two dimensions is possible and helpful for visualization purposes, these images do not reflect the exact distances between words in the high-dimension space.

these observations should be understood in combination with other results, e.g., similarity spectrums demonstrated in the previous section.


For SG model, prior knowledge is introduced in a similar way, with the difference being that context words are predicted instead of the current word.

when learned from the context, em- beddings are updated in the same way as in normal CBOW and SG models.


Hierarchical softmax is a good choice for reducing the computational complexity when training probabilistic neural network language models.

for context learning on the left part of Figure 1, we continue using hierarchical softmax based on Huffman coding tree (Mikolov et al., 2013a).


of 0.125 for both positive and negative polarities.

there are 9 different degrees for a word to be annotated for the two sentiments.


Since retrofitting only updates those words in the external sources, e.g., LDA word list or lexicons, it is very sensitive to the quality of the corresponding sources.

it can be observed from our experiment that unannotated knowledge, i.e., topic distributions, is not an effective source as a good guidance.


Intuitively, we know the 4-th and 5-th sentence can give strong supporting ideas to illustrate why the author concludes the story with the last paragraph.

it proves that our attention mechanism on sentences captures the key sentences to represent essays indeed.


For the third sentence, it is ambiguous without further context from other sentences.

entity abstraction is a delicate balance between specificity and correctness.


Thus, we can encode this binary information as an additional dimension in the entity representation.

we formally define r e as the entity representation.


Since we have two different lexicons, we get two separate one-hot sentiment vectors, each with a dimension of 3.

the sentiment representation is the concatenation of the two vectors, a total dimension of 6.


Most of the highlights have this characteristic, not relying on any previous or subsequent sentences to make sense.

we frame the extractive summarisation task here as a binary sentence classification task, where we assign each sentence in a document a label y ∈ 0, 1.


Location Authors such as Kavila and Radhika (2015) only chose summary sentences from the Abstract, Introduction or Conclusion, thinking these more salient to summaries; and we show that certain sections within a paper are more relevant to summaries than others (see Section 5.1).

we assign sentences an integer location for 7 different sections: Highlight, Abstract, Introduction, Results / Discussion / Analysis, Method, Conclusion, all else.


At each step we plot the difference in log-probability between our model and a uniform baseline model.

a marker above 0 means that our model assigned the student's answer a probability higher than chance.


Explicit feedback (including for an EX card) sets f t−1 to a one-hot representation of the correct answer.

f t−1 gives the set of "positive" options that we used in the RG update vector, while O t−1 gives the set of "negative" options, allowing the LSTM to similarly update its hidden state from h t−1 to h t to reflect learning.


However, since the sampling of each tree t depends on θ, and we need to resample all n parse trees before sampling θ, this Markov chain can be slow to mix.

we employ collapsed Gibbs sampling by integrating out θ.


We can use this quantity in equation 8to compute the priority of these "aggregated" agenda items.

this algorithm is a kind of branch-and-bound approach to the combinatorial optimization problem.


In such cases, the maximum possible recall is 88.2 and 82.3 on GeoQuery and Jobs, respectively.

we also measure the effect of adding a domain-specific lexicon, which maps semantic constants like maine to the noun "maine" for example.


, l m−1 , and ρ is the log probability of the first m−1 righthand symbols, and the logical form is x where f m (x ) = x.

we can write I (Du,l u ,l u+1 ) + O (C,i ,j ,x ) , but this is not possible by the definition of O (A,i,j,x) in equation 7, as we would have found a derivation with a strictly better objective.


We found that neural embeddings produced by current methods do not provide a suitable contextual similarity.

we define a new approach based on a Siamese Network, which produces word representations while learning a binary text similarity.


Unfortunately, the study above showed that standard ways to model context are not effective.

we propose a novel application of Siamese Networks to learn word vectors in context, i.e., a representation of a word conditioned on the other words in the sentence.


As in (Croce et al., 2011), we observed that embeddings learned from raw words are not the most effective in the TK computation.


This state encodes the word given its sentential context.

the first layer, BiGRU , maps the sequence of input vectors (x 1 , ..., x T ), into a sequence of states (s 1 , ..., s T ), the second, BiGRU , transforms those states into (s 1 , ..., s T ), and the third, BiGRU , produces the final representations of the words in context (s 1 , ..., s T ).


Domain independent lexicons or general word embedding model may not perform well in financial domain.

it is necessary and important to built sentiment lexicons and word embeddings specifically for stock market.


However, we were curious about why the association between the lower PID values and the AD diagnosis cannot be observed on DementiaBank.

we investigated this issue and found that the DementiaBank interviews have certain additional characteristics that contribute to the automatic proposition count being too high.


In SQuAD, every question is answerable from the text, which is why these models assume that there exists a correct answer span.

we modify an existing model in a way that allows it to decide whether an answer exists.


There are very limited number of successive empty elements in between two successive overt words.

we can treat all successive empty elements governed by same head as one word.


The set of complete or sibling spans has O(n 2 ) elements, while the set of each type of incomplete spans has O(n 2 ) elements.


The second case is worse and needs time of O(n 3 ).


7 The DTA provides more than 2447 lemmatized and POS-tagged texts (with more than 140M tokens), covering a time period from the late 15 th to the early 20 th century.

it covers the developments of German from (late) Early New High German to Contemporary New High German.


According to Steen (2010, p. 2) "the identification of metaphoric language has become a matter of controversy".

we did not want to rely solely on our own intuitions, but identify metaphoric change of words via annotation.


However, it can be understood in comparison with it: We can understand abstract change in terms of physical or local change.

we fill in 1 in the column headed by 'M2 is metaphorically related to M1', judging M2 to be metaphorically related to M1.


Hence, neither meaning has a metaphoric relation to the other.


At each time step, the decoder RNN receives c i in addition to the previously-output word.

the hidden state 2 at time i of the decoder is defined as What is the homepage of the cinema Cinéma Chaplin in Paris?


The target words in the other language are obtained by looking up that word in a bilingual dictionary.

the input to their model is monolingual data in both languages and a bilingual dictionary.


Coreference evaluation metrics are hard to optimize directly as they are nondifferentiable functions, not easily decomposable into elementary decisions.

most approaches optimize objectives only indirectly related to the end goal, resulting in suboptimal performance.


Coreference resolution can be regarded as a clustering problem: each cluster corresponds to a single entity and consists of all its mentions in a given text.

it is natural to evaluate predicted clusters by comparing them with the ones annotated by human experts, and this is exactly what the standard metrics (e.g., MUC, B 3 , CEAF) do.


the member function I(m ∈ S)) with the corresponding posterior probabilities (p(m ∈ S)) computed by the model.

non-differentiable functions used within the metrics (e.g.


As T → 0 the softmax converges to the indicator function that chooses is the converging point of the following softmax as T → 0 (see Figure 2): (Kirkpatrick et al., 1983).

we propose to represent each S u as a soft-cluster: where, as defined in Section 3, E u is the potential entity that has m u as the first mention.


Unfortunately, annotating sufficient amounts of such data is often costly and labor intensive.

for many NLP applications even resource-rich languages like English have labeled data in only a handful of domains.


By fixing the representations of similar conflicting features to similar vectors, AE-SCL-SR provides a strong bias for h 1 and h 2 to be similar, as its only way to bias the predictions with respect to these features to be low is by pushing h 1 and h 2 to be similar.

under AE-SCL-SR the vectors that encode the non-pivot features of documents with similar pivot features are biased to be similar to each other.


However, performing reliable dialect identification to properly route text to the appropriate segmenter may be problematic, because conventional dialectal identification may lead to results that are lower than 90% .

building a segmenter that performs reliably across multiple dialects without the need for dialect identification is desirable.


Moving from a limited domain dialogue system to an open domain dialogue system raises some issues.

it is important to build an open domain dialogue system that can make as much use of existing abilities of functioning from other domains.


We then feed the embedded sequence w = {w 1 , w 2 , ..., w T } into a bidirectional LSTM (Graves and Schmidhuber, 2005).

each input x t is associated with a contextualized feature vector where − → f t and ← − f t represent the left and right context at time step t of the sequence.


For example, JOURNAL often co-occurs with PAGES but JOURNAL is not strictly accompanied by PAGES (Anzaroot et al., 2014).

we regularize the state transition log-potential with a low-rank constraint, forming an embedding matrix wherein state transition interaction scores are mediated through low-dimensional state embeddings rather than a fully unconstrained parameter matrix.


The CLEF eHealth dataset holds a different set of constraints than the citation data, and its input sequences are not strong local indicators of the labeling sequence.

our model shows stronger performance over the Markovian baseline for this dataset.


The objective that SGNS minimizes is In the limit of large d, Levy and Goldberg (2014) found the following closed-form solution to Eq 1: The first term can be seen as an empirical estimate of Pointwise Mutual Information (PMI): PMI(w, c) = log P (w,c) P (w)P (c) .

the matrix M that SGNS factorizes can be constructed from For computational convenience, Levy and Goldberg (2014) suggested a sparse and consistent alternative called Shifted Positive PMI (SPPMI): (2) Levy and Goldberg (2014) showed that using such sparse representation, word and context embeddings could be efficiently obtained using a truncated singular value decomposition.


Since the composition of two orthogonal matrices is still orthogonal, we obtain the SVD of S 0 as U = U U , V = V V .

note that this transformation is exact since in our approximation, S 0 = W C was already of rank d. W C = U ΣV .


This can not be carried out in a single step, because the online SVD method sketched in Algorithm 2 only supports the addition either rows or columns (here presented for columns).

we first concatenate U ΣV and x horizontally and perform a rank d truncated SVD.


However, the KL divergence loss tends to be significantly small during training (Bowman et al., 2015).

s, the decoder does not take advantage of information from the latent variable z.


18 new entities are navigated to, on average.

the datasets are naturally very dynamic and sensitive to change.


For example, in Figure 1, the query sentence discusses "Zywiec" selling a product and profiting from these sales, but the local contextual information is ambiguous as more than one entity type could be involved in a sale.

the baseline model mistakenly tags "Zywiec" as a person (PER) instead of the correct tag, which is organization (ORG).


However, across all the data in our experiments (Section 3.1), we find that an average of 35.43% of named entity mentions in each document are repeats and, when a mention appears more than once in a document, an average of 98.78% of these mentions have the same type.

one may use the documentlevel context to overcome the ambiguities of the local context while introducing little noise.


However, contexts from other sentences in the same document containing "Zywiec" (e.g., s q and s r in Figure 2), such as "'s 1996 profit..." and "would be boosted by its recent shedding...", indicate that "Zywiec" ought to be tagged as ORG.

we incorporate the document-level supporting evidence with the following attention mechanism (Bahdanau et al., 2015).


Additionally, Reimers and Gurevych (2017) report that neural models produce different results even with same hyper-parameters due to the variances in parameter initialization.

we run each model ten times and report the mean as well as the maximum F1 scores.


The pooled features are then transformed to predictions over the output vocabulary V, by linearly mapping them with a matrix E ∈ R |V|×f L to the vocabulary dimension |V|, and then applying a soft-max.

the probability distribution over V for the i-th output token is obtained as Alternatively, we can use E to project to dimension d t , and then multiply with the target word embedding matrix used to define the input tensor.


As opposed to adversarial examples in computer vision, which are computed on continuous data and can thus easily be imperceptible if desired, adversarial attacks in NLP entail the necessity to perform discrete and perceptible changes to the data.

attack methods for computer vision such as the Fast Gradient Sign Method (FGSM) ) cannot be directly applied to NLP.


Third, we present a detailed comparison between human and machine reading comprehension, giving insights when and why our systems fail.

these insights are important for future research towards enhancing machine comprehension systems loosely inspired by human processing.


adding the distracting sentence as first instead of last sentence, made the adversarially-trained models to fail almost as badly as without adversarial training.

average Ensemble CNN 31.59 32.07 RNN-LSTM 32.61 32.17 Table 6: adversarial accuracies on the validation set under the sentence-level white-box attack based on removal of the plot sentence with highest attention (averaged results of nine models).


Although researchers have developed techniques for acquiring CKB from raw text with patterns (Angeli and Manning, 2013), it has been pointed out that some sorts of knowledge are rarely expressed explicitly in textual corpora (Gordon and Van Durme, 2013).

researchers have developed curated CKB resources by manual annotation (Speer et al., 2017).


In the reverse direction, we replace the relation label r with r ′ ; namely, the output is t 1 , and the input is ⟨t 2 , r ′ ⟩.

in our CKB generation model, the vocabulary size of the relation is twice that of the original relation set.


Once the human has revised these samples, they are used for improving the MT system, via incremental learning.

a key element of AL is the so-called sampling strategy, which determines the sentences that should be corrected by the human.


Once corrected, the MT system adapts its models with these samples.

the AL protocol applied to unbounded data streams is as follows (González-Rubio et al., 2012): first, we retrieve from the data stream S a block B of consecutive sentences, with the function getBlockFromStream(S).


For estimating the human effort, we simulated the actions that the human user would perform when using the IMT system.

at each iteration the user must search in the hypothesis the next error, and position the mouse pointer on it.


On the other hand, INMT systems usually respond much better to the human feedback than interactive SMT systems (Knowles and Koehn, 2016;Peris et al., 2017).

the differences between SMT and NMT were enlarged even more.


This is very important for companies if we consider that attracting new customers is a time and cost-intensive task.

it is often preferable for companies to focus on the existing customers in order to prevent losing them instead of trying to acquire new ones.


We describe our experiments in Section 5 and finally conclude in Section 6.

this work is an intersection of (i) churn detection in social media, (ii) multilingual churn detection and (iii) churn detection in chatbots we present the related work for each domain sepa-rately.


Lang is a 2-letter abbreviation of the source language.

we discuss the creation of 4 different datasets, namely EN T , EN C , DE T and DE C 1 .


This new subset has the advantage of not being biased by the predefined filter keywords as opposed to DE T F ILT ER .

we can reduce the overall bias of our dataset by labeling DE T BOOT and concatenating it to DE T F ILT ER .


However, creating and labeling data is a tedious task for the user and might lower the quality of our text-label pairs.

we choose to present the chatbot interface as a game to make it more user-friendly.


However, an increase of 2.03% of the mean still represents an important improvement over the state-of-the-art.

we prove that our novel architecture provides an efficient way to detect churn in social media.


In this paper, we argue that the position-aware representations are beneficial to this task.

we propose a hierarchical attention based position-aware network (HAPN), which introduces position embeddings to learn the position-aware representations of sentences and further generate the target-specific representations of contextual words.


However, this operation is not always reasonable and sometimes the adjunct word may be far away from the target word.

we introduce position embeddings when modelling the sentence and further generate the positionaware representations.


2The output of Source2aspect attention is only used for information fusion.

we remove the fusion operation and Source2aspect attention while removing the Source2context attention.


Nonetheless, one could easily infer that water is involved in the process, since the word hydrolyzed refers to water.


Loss Function As opposed to the loss function previously presented, here we would like to preserve relatedness among prediction tasks when they are actually related only.

we use task grouping feature to recognize T disjoint sets of entities as prediction task groups.


Oxygen, NADPH, NADP+ and more occurs in less than 3% of the reactions).

many classes of entities have very little training examples.


We use this property to rank the candidate phrases extracted in the previous step, by measuring their distance to the original document.

semantic relatedness between a candidate phrase and its document becomes a proxy for informativeness of the phrase.


We show that submodular optimization models for MDS can yield well-performing models for TLS, despite the differences between the tasks.

we can port advantages such as modularity and separation between features and inference, which current TLS models lack.


For Human, we select quatrains containing the given keywords.

we obtain 240 quatrains (20*6*2) in total.


Style-SC is not suitable for BLEU, because we can't expect LDA to predict a correct style label by a short keyword.

style-sC is only tested under Human Evaluation.


For the poem generated by SC, the word "autumn" in the second line is selected for successive generation.

a word "fallen leaves" is generated in the fourth line.


The analysis window of the global memory is limited to the current and previous cell memories of each LSTM encoder, whereas our approach abstracts the shared memory update dynamics away from the ties of the encoding dynamics.

our approach enables post-fusion and retrospective reanalysis of the entire cell memory history of all encoders at each analysis iteration.


The results presented in previous section showed that ns-CRP performs the worst on Austronesian and Austro-Asiatic datasets.

we present the cluster evaluation results only for these two datasets in table 4.


This can be clearly seen in the word similarity experiments in Section 3.2, where fasttext achieves significantly better results than glove for the original embeddings, yet the results for their best post-processed embeddings are at par.

if one simply looks at the results of the original embeddings, they might wrongly conclude that fasttext is vastly superior to glove at encoding semantic similarity information, but this proves to be a mere illusion after applying our post-processing.


A configuration is a scenario that additionally includes an index, either indicating the target noun pair (speaker configuration) or the adjective that was provided to the listener (listener configuration).

while scenarios are just lists of adjectives and nouns, there are #codenames 2 possible speaker configurations and #clues possible listener configurations.


Producing sentences which are perceived as natural by a human addressee-a property which we will denote as fluency 1 throughout this paper -is a crucial goal of all natural language generation (NLG) systems: it makes interactions more natural, avoids misunderstandings and, overall, leads to higher user satisfaction and user trust (Martindale and Carpuat, 2018).

fluency evaluation is important, e.g., during system development, or If access to a synonym dictionary is likely to be of use, then this package may 3 be of service.


This results in difficulties for current reference-based evaluation, especially of fluency, causing wordoverlap metrics like ROUGE (Lin and Och, 2004) to correlate only weakly with human judgments (Toutanova et al., 2016).

fluency evaluation of NLG is often done manually, which is costly and time-consuming.


In contrast to SVR (line 1), ROUGE-LM (line 2) has only the same requirements as conventional wordoverlap metrics (besides a large corpus for training the LM, which is easy to obtain for most languages).

it can be used in the same settings as other word-overlap metrics.


Finally, for given references, we proposed the reference-based metric ROUGE-LM, which consists of a combination of WPSLOR and ROUGE.

we were able to obtain an even more accurate fluency evaluation.


This will be illustrated at the end of Section 5.

fast libraries like CuDNN (Chetlur et al., 2014) can be used directly.


Word occurrence frequencies have a typical Zipfian nature (Manning et al., 2008), with many rare and few highly frequent terms.

representing the long tail of rare terms with short embeddings should greatly reduce memory requirements.


First, drawing general conclusions on the quality of em-bedding models only based on the performance of specific approaches, i.e., examining the extrinsic suitability of models, is error-prone (Gladkova and Drozd, 2016;Schnabel et al., 2015).

to come to general conclusions one needs to investigate general properties of the embedding models itself, i.e., examine their intrinsic suitability.


In summary, the reduced model quality generally is a consequence of the less frequent words not being trained sufficiently or even not at all.

they do not appear in the dictionaries of the model.


We designed one setting for evaluating the effect of using constituents instead of words (constituent-base vs. word-base) and another to evaluate the effect of using attention mechanism on top of vector training modules (uni-vs. bi-attention).

we conducted four experiments on both datasets or eight experiments in total.


In contrast, the speech sound of a Chinese character is represented by a single syllable in Pinyin consisting of two or three parts: an initial (optional), a final or compound finals, and tone 1 (Table 1).

phonetic similarity approaches designed for Indo-European languages often fall short when applied to Chinese text.


lar to the first character of the given word, which in turn increases the number of candidates within the distance.

the probability of including the labeled gold standard words in the results increases.


This editorial did not affect annotators of either ideology.

it meets our conditions of being of low argumentation quality.


Given a tweet t with a sequence of words w 1 , ..., w n , our goal is to identify the possible entity mentions in the tweet t. Specifically, every word w i in tweet t requires a label to indicate that whether it is an entity mention word or not.

we view it as a traditional named entity recognition (NER) problem and use BIO tagging schema.


The final matching score is conducted based on the patterns.

the concrete matching information is captured.


In contrast, M-CNN captures the compositional and abstract meaning of a whole text.

we produce the semantic matching signals using both models to capture different aspect of semantics that are useful for entity linking.


Formally, we denote N as the number of mentions identified by a system and M as the correctly linked mentions.

precision = M N , recall = M N and F 1 = 2 * precision * recall precision+recall .


As will be shown in Section 6, the above strategies are effective on the Ubuntu task, but not on the collaborative-style, database-dependent Co-CoA task.

for the latter, we investigate additional Should-Change strategies including Random Inputs (changing each word in the utterance to random ones), Random Inputs with Entities (like Random Inputs but leaving mentioned entities untouched), and Normal Inputs with Confusing Entities (replacing entities in an agent's utterance with distractive ones) to analyze where the model's robustness stems from.


Since task oriented systems primarily focus on completing a specific task, they usually do not allow free flowing, articulate conversations with the user.

there has been considerable effort to develop non-goal driven dialogue systems, which are able to converse with humans on an open domain (Ritter et al., 2011).


We define an utterance as a sequence of words {w t } T t=1 .

c i can also be viewed as a sequence of words by concatenating all utterances in c i .


In contrast, in the validation and test set, for every context c i , there exists one positive triple providing the ground-truth response to c i and nine negative triples with unbefitting responses.

in these sets, the ratio between positive and negative triples per context is 1:9 which makes evaluating the model with information retrieval metrics such as Recall@k possible (see Section 6).


In total, compared to the DE-biGRU baseline, our model (AK-DE-biGRU) achieves 10% of improvement in terms of the R 10 @1 metric.

the results clearly suggest that both the attention mechanism and the incorporation of domain knowledge, are effective approaches for improving the dual encoder architecture.


The objectives of jointly learned tasks are often different and the mapping from the shared representation to the output for each task is learned independently (Liu et al., 2015;Hashimoto et al., 2017).

the relation between classification schemes is not directly considered.


Calculating the posterior probability We assume that every human's response t n is independent from the previous dialogue history, conditioned on the immediately previous message, and chatbot's question s n is independent unconditionally.

the posterior can be computed recursively: where z N is the fact used in the N th turn.


Calculating the entropy We make an assumption that the number of facts K assigned to the human is known in advance.

we can consider only probabilities where F is of a particular known size.


It has to apply attention at every step, even when responding to messages which are not relevant to any of the facts.

it always reveals something about the personality (unless the attention is uniform, generally hard to achieve in practice).


In GI-Dropout, a word of higher importance score has greater possibility to be dropped.

models are forced to learn unobvious features and will achieve better performance in prediction.


We observe that adding a component-specific vector to the recurrent cells in the bottom layer of the decoder is the most effective, and results in BLEU scores superior or onpar with the other variants across the four language pairs.

we use this model variant in all experiments for the rest of the paper.


These alternate output labels are easier to predict than the full set of supertags, and these new output labels are related to the original supertag in a linguistically relevant way.

we train on the same training set but with alternate output labels, each forming a different task.


For each word in a word pair, we "contextualize" the word by selecting a sentence in which the word appears, and ensure that the resulting contextualized word can be translated into the other word.

if a polysemous word occurs in multiple word pairs with distinct translations, it will be accompanied with different contexts that correspond to each translation.


However, a model running in the other direction can penalize this omission, as found by Goto and Tanaka (2017).

we obtain the back-translation probability p back of each sampled candidate paraphrase, and define the final score for each candidate paraphrase as the joint probability p * = p generate * p back , which is the sum of negative log-likelihood.


We found that A(B | A) was proportional to the surprisal of B prior to adaptation (see Supplementary Materials).

for three structures X, Y and Z, A(Y | X) could be greater than A(Z | X) merely because Y was a more surprising structure to begin with than Z.


The only exception was the class of all RCs, where the property shared by all sentences in this class (the presence of a gap) was not overtly observable.

it is possible that the similarity between members of most of the classes we tested was being driven entirely by the presence of these function words.


Although advanced L2s have much fewer infelicities than learners, they also have more in the QU and CD classes (7% and over 9% respectively) than in the others (5-6%).

as with Hypothesis 1, Hypothesis 2 is largely borne out by the data, and we find additional evidence that the IP system of English is particularly challenging for beginning to intermediate learners.


13.8 26.0 1.4 0.8 20.3 16.9 FULL 42.7 38.7 5.9 33.3 39.6 39.5 the same image features and a decoder architecture as the BUTD model.

when using the standard beam search decoding method, BUTR does not improve over BUTD.


However, these models treat names and entities no more than the tokens used to mention them.

these models are unable to well represent names in nar-rative understanding task because the word "John" in a given story can be very different from the word "John" in another narrative.


While these rich representations improve the performance on several tasks such as coreference and reading comprehension, they rely on explicit mentions of entities in text as available in toy datasets such as bAbi (Weston et al., 2015).

it is difficult to apply these representations in a dialogue setting due to the sparseness of name mentions in dialogue, as well as the lack of explicit conversation connections between characters (as available in movies) (Azab et al., 2018).


False and true friends coactivation In bilingual speakers, false friends show an inhibitory effect of the L1 meaning in L2 tasks, but they do not affect the final accuracy of the task completion.

in our cross-lingual vectorial space, false friends should not have higher similarity score than their real translations, but they should be included in the top translations, i.e.


In this case, it is important to remember that w 1 has a false friend w 2ff , so we know that accessing w 2 is more effortful since, for a bilingual speaker, w 2ff will also be activated.

we can assume that a normal pair of words (w 1n , w 2n ), a pair of translated words that have no false friend, are closer in space than (w 1 , w 2 ) precisely because (w 1n , w 2n ) is not inhibited by a false translation as in the case of (w 1 , w 2 ).


The online English-Italian dictionary was not comprehensive: not all the meanings were reported, unlike the English-Spanish or English-French dictionaries.

sometimes a false friend was not reported by the English-Italian dictionary, but was found with the help of the other bilingual dictionaries.


For the concept-based system, on the other hand, we do not have enough evidence to reject the null hypothesis as p > 0.025 and T(296)<t-value.

in this case, we must conclude that the normal translations are as similar as the real translations.


the form progestiinittä, predicted by both systems, is grammatical, though not the form given by UniMorph.


Our analysis is limited to languages included the shared task, those for which the top systems have a non-trivial number of errors, and those for which we have sufficient linguistic expertise.

our final sample of twelve languages only includes two major language families, Indo-European and Uralic, the latter represented by Finnish and Hungarian.


Finally, it is natural in foreign language learning that a human learns foreign words by looking up their meanings in the native language (Hulstijn et al., 1996).

learning such correspondence essentially mimics human learning behaviors.


Because of the differences between language model and NLU task objectives, the problem of lack of concern on some key words exists.

in this paper, we propose a method called reverse mapping bytepair encoding, which maps named-entity information and other word-level linguistic features back to subwords during the encoding procedure of bytepair encoding (BPE).


However, it is originally designed to handle open-vocabulary problem in machine translation, basing on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).

compared with semantic characteristics, morphological and compounding characteristics are more considered.


However, linguistic features contain different levels of information compared with words and wordpieces.

we evaluate two different training processes mentioned in Section 3.3.


As we can see, the natural lexical choices for expressing the LOCATION relation between the picture and the wall and the TOPIC relation in German have diverging literal translations to English.

the empirical study of cross-linguistic commonalities and differences between form and meaning calls for a common metalanguage to describe the relations between mentioned events and entities, as marked by case and adpositions.


The word 'disaster' may call to mind very different things depending on the latest type of disaster.

the word may evoke 'nuclear disaster' in a reader in 2011 (e.g., driven by the Fukushima incident).


When looking at a specific word, we wish to focus on its relevant meaning at a particular time.

the temporal word embeddings are created using data from a large temporal corpus.


Though it is surely a powerful event and one that is related to many world leaders, it is hard to describe it as a turning point for either Bill or Hillary Clinton.

we attempt to learn which are the most relevant events for a given word.


Nonetheless, the ByKNNCls method wins the three most important metrics: relevance, ranking, and effectiveness.

in the eyes of the evaluators, this method gives the most relevant timelines compared to all others, and maybe most importantly, provides novel information.


Additionally, the training data of the classifier can be generated using any embedding.

we perform an empirical evaluation comparing all combinations of embeddings for representing the features and the training data employed by the classifier (Table 2).


For this application, we give equal weights to precision and recall.

the crosslingual STS of sentences e and f using YiSi-2 in this work can be expressed as follows: w (e) = idf (e) = log(1 where s(e, f ) is the cosine similarity of the vector representations v(e) and v(f ) in the bilingual embeddings model.


Zipporah combines fluency and adequacy features to score sentence pairs; adequacy features are derived from existing parallel corpora, and the feature combination (logistic regression) is optimized on in-domain parallel data.

zipporah can be seen as a fully supervised method.


1 The increased parameter sharing ability of such models is especially relevant for learning embeddings of rare and unseen words.

the importance of subword-level knowledge should be even more pronounced in low-data regimes for truly low-resource languages.


Both models both are parameterized with recursively structured neural networks that have access to the complete history of generation events.

the factorization of the tree probability is justified by the chain rule.


We define an equivalence class (i, q) containing all configurations c such that the first index i of β equals i and the size of stack |σ | equals q.

we rewrite cumulative transition scores as: (3) We also consider a score function based on the nestedness property of shift/reduce derivations.


We use normalized scores via softmax to interpolate head vectors.

for each vector word X e [i] we obtain an expected head vector Y e [i], which will be used hereafter.


In this paper, G is trained with a specific 1-pair loss defined on sentence alignment score, instead of Jensen-Shannon divergence  between two data distributions, which could avoid the vanishing gradient problem in GANs.

the high accuracy of the proposed discriminator would not make negative impact on G. In this section, we will study characteristics of the proposed approach and report some detailed ex-  perimental results.


Existing methods that apply equivalence constraint theory to generate code-switching sentences (Li and Fung, 2012;Pratapa et al., 2018) may suffer performance issues as they receive erroneous results from the word aligner and the partof-speech (POS) tagger.

this approach is not reliable and effective.


The catastrophic forgetting issue arises when we train one language after the other.

we solve the issue by applying a multi-task learning strategy.


Although back-translation with MAP prediction has been proved to be successful, it suffers from several apparent issues when trained with maximum likelihood estimation (MLE) only, including exposure bias and loss-evaluation mismatch.

this method often fails to produce the optimal synthetic sentences for the subsequent training.


Finally, the reward of the generated translationx l 1 on n-gram matching is defined as: where r 1 ranges from zero to one and it is symmetrical when switchingx and x. Semantic adequacy We want the model can adequately convey the meaning of the source sentence to the target as much as possible.

we introduce another crucial reward function that is used to measure the semantic adequacy of the generated translations.


Interestingly, these results hold true for the Transformer despite its indifference to the absolute word order.

word distance in itself is not what makes such phenomena challenging, contrary to what one might expect from the definition of LDD.


It is to be noted that at training time, the network was only given full captions and not isolated words.

if the network is able to retrieve images featuring instances of the target word, it shows that implicit segmentation was carried out at training time.


On average, they are 1.35 peaks per word for the trained model against 0.1 peak per word for our baseline condition (untrained model), showing that cosine evolution is linear in the latter but not in the former.

in our baseline condition (untrained model), each MFCC vector equally contributes to the final representation, whereas in our trained model some MFCC vectors are more de-6 consisting only of randomly initialised weights cisive for the final representation than others.


But a large number of equations are invalid as they violate constraints such as unit consistency.

our framework uses integer linear programming (ILP) to constrain the equation space.


However, when training a parser on the source language, the encoder not only learns to embed a sentence but it also carries language-specific properties, such as word order typology.

the parser suffers when it is transferred to a language with different language properties.


Besides, the multilingual variant of BERT (mBERT) (Devlin et al., 2019) has shown to perform well in cross-lingual tasks (Wu and Dredze, 2019) and outperform the models trained on multilingual word embeddings by a large margin.

we consider conducting experiments with both multilingual word embeddings and mBERT.


However, for more complex sentences, it becomes challenging to identify relevant subclauses or to handle nontrivial constructions like conditional clauses (see aquarium example).

the EMOTIVE rule-based responses, while grammatical, tend to be overly verbose.


At the time of analysis (June 2018), over 25% of the YouCook2 videos had been removed from YouTube, and therefore we do not consider them.

all our experiments operate on a subset of the YouCook2 data.


However, in realistic applications of grounding, multiple users will repeatedly refer to the same set of objects.

past referring expressions for objects can provide strong signals for grounding subsequent referring expressions.